01
File state acts as the integration boundary
Processing does not depend on the browser or a long-running request. File state tells the system when an image is waiting, completed, or needs failure handling.
maskmill.com
AI background removal integrated into the maskmill.com order workflow
maskmill.com needed automation that accepts user-uploaded images, creates a transparent mask, produces finished PNG and JPG outputs, and leaves production staff with correction-ready material. The solution was built into the existing order, payment, file and production workflow instead of becoming a separate tool.
10s
worker polling cycle
50MP
image processing class
3
output asset types
1
unified order pipeline
Traditional approach
A separate AI tool with manual handoffs.
- Customer data, payments, and files split across steps
- Manual retrieval of finished images and masks
- Correction orders require recreating the original work package
- Customer has no real-time processing feedback
Snaips approach
AI embedded into the production system, not bolted on outside it.
- After payment, images move automatically into the processing queue
- Background worker generates the mask, transparent PNG, color-backed JPG, and preview
- Mask is stored separately for human correction workflows
- Real-time status updates keep the customer in the loop
Scope: Order form, upload validation, post-payment queueing, background processing, image compositing, cloud storage, previews, and correction workflow.
.NET
Blazor / Razor
EF Core
Background Service
SignalR
ImageMagick
Cloud Blob Storage
External AI Image API
Pipeline
How background removal runs in production
The solution is built as a queue where each file has its own state. Failed jobs do not loop forever, and successful images move straight into the customer download flow.
- 1
Order and settings
The customer uploads images and selects crop and background color options. Those settings are stored directly on the order.
- 2
Validation
Oversized or unsupported images are converted to fit the processing limits before AI handling starts.
- 3
Queueing
After payment, files are marked as waiting for processing. A background service picks up the queue automatically.
- 4
Mask and compositing
An external image segmentation service returns a mask. The system composites it with the original image and builds the final production assets.
- 5
Delivery and corrections
Finished files are stored for download and the mask is archived separately so any human correction can start from the right source material.
Architecture
Why the implementation holds up in real production
02
Image processing turns AI output into deliverables
AI produces the mask, but the production system creates the real deliverables: alpha compositing, color background, full-size PNG, and lightweight preview.
03
Human work remains available where it matters
Automation does not block manual quality work. Original images and alpha masks are stored so a correction order can be handed to production as a ready package.
The bottom line.
AI features become valuable when they work inside a real process — pricing, payment, file handling, user messaging, production and corrections. In this implementation, AI background removal is not a demo; it is a production part of maskmill.com.
Interested in a similar AI implementation for your service?